| --- |
| language: |
| - en |
| - hi |
| license: apache-2.0 |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-classification |
| - table-question-answering |
| - text-generation |
| tags: |
| - finance |
| - synthetic |
| - banking |
| - india |
| - transactions |
| - bank-statements |
| - document-ai |
| pretty_name: Indian Bank Statement Synthetic Dataset |
| --- |
| |
| # Indian Bank Statement Synthetic Dataset |
|
|
| Synthetically generated Indian **business bank statements** with realistic transaction patterns, proper banking workflows, and India-specific features. Available in **scanned PDF** and **digital JSON** formats. |
|
|
| **Scope:** Current Accounts (business banking) only. Does not include personal/savings accounts. |
|
|
| ## Dataset Details |
|
|
| - **Curated by:** AgamiAI Inc. |
| - **Language(s):** English, Hindi (romanized) |
| - **License:** Apache 2.0 |
| - **Repository:** https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements |
| - **Website:** https://www.agami.ai |
|
|
| **Note:** Contains only legitimate transactions (no fraud patterns). |
|
|
| ## Uses |
|
|
| ### Suitable For |
| - Document AI and OCR training |
| - Information extraction (account numbers, balances, transactions) |
| - Transaction categorization and classification |
| - Financial document understanding |
| - Table extraction and parsing |
| - Named Entity Recognition (NER) |
| - Testing data processing pipelines |
| - Educational purposes |
|
|
| ### Not Suitable For |
| - Fraud detection or AML (no fraudulent patterns) |
| - Production compliance or regulatory reporting |
| - Credit decisions (lacks real creditworthiness signals) |
| - Personal banking AI (business accounts only) |
|
|
| ## Dataset Structure |
|
|
| ### Statement Formats |
|
|
| **Type 1: Separate Debit/Credit Columns** |
| | Date | Description | Debit | Credit | Balance | |
| |------|-------------|-------|--------|---------| |
| | 01/01/2024 | UPI-Vendor | 450.00 | - | 25,780.50 | |
| | 02/01/2024 | NEFT Credit | - | 50,000.00 | 75,780.50 | |
|
|
| **Type 2: Single Transaction Column** |
| | Date | Description | Transaction | Balance | |
| |------|-------------|-------------|---------| |
| | 01/01/2024 | UPI-Vendor | -450.00 | 25,780.50 | |
| | 02/01/2024 | NEFT Credit | +50,000.00 | 75,780.50 | |
|
|
| ### JSON Structure |
|
|
| ```json |
| { |
| "bank_name": "Paramount Banking Corporation", |
| "account_holder": "CYIENT TECHNOLOGIES", |
| "account_holder_address": "F-346\nThird Floor\nHinjewadi\nPune\nMaharashtra\n520018", |
| "account_number": "90823789756", |
| "ifsc_code": "PARA0761987", |
| "micr_code": "899946557", |
| "branch_name": "PUNE HINJEWADI", |
| "branch_code": "6738", |
| "account_type": "CURRENT ACCOUNT- GENERAL", |
| "currency": "INR", |
| "customer_id": "134743833", |
| "opening_balance": 158458.03, |
| "closing_balance": 64424.49, |
| "start_date": "2024-01-01", |
| "end_date": "2024-03-31", |
| "statement_date": "2025-11-20", |
| "interest_rate": 2.83, |
| "transactions": [ |
| { |
| "date": "2024-01-01 12:40:40", |
| "value_date": "2024-01-01", |
| "description": "NEFT Dr-471179370408-HDFC0009038-RIDDHI RAVAL", |
| "cheque_no": "862512", |
| "debit": 13932.79, |
| "credit": null, |
| "balance": 144525.24, |
| "branch_code": "3421", |
| "failed": false |
| } |
| ] |
| } |
| ``` |
|
|
| ### Transaction Types |
|
|
| - **UPI**: Unified Payments Interface (DR/CR) |
| - **NEFT**: National Electronic Funds Transfer |
| - **RTGS**: Real Time Gross Settlement (high-value) |
| - **IMPS**: Immediate Payment Service, salary transfers |
| - **Cheques**: Chq Paid, By Clg (Clearing) |
| - **Cash**: Withdrawals and deposits |
| - **ATM**: ATM withdrawals |
| - **Service Charges**: Bank fees |
| - **Reversals**: Failed transaction reversals |
|
|
| ## Dataset Creation |
|
|
| ### Why This Dataset |
|
|
| India's digital payment ecosystem is rapidly growing, but publicly available datasets for training AI models on Indian business banking documents are scarce due to privacy constraints. This dataset provides production-quality synthetic data for: |
|
|
| - Training document AI on Indian bank statement formats |
| - Testing OCR and information extraction systems |
| - Building fintech applications without real customer data |
| - Both scanned (unstructured) and digital (structured) formats |
| - India-specific payment systems (UPI, IMPS, NEFT, RTGS) |
|
|
| ### Data Generation |
|
|
| **Fully synthetic** - no real customer information: |
| - Probabilistic modeling of realistic business transaction patterns |
| - Proper debit/credit flows with accurate balance calculations |
| - India-specific features: UPI references, IFSC/MICR codes, Indian business names |
| - Business entities: IT companies, manufacturing, retail, financial services |
| - Geographic coverage: Mumbai, Delhi, Bangalore, Pune, Chennai, Kolkata, Hyderabad |
| - Both scanned PDFs and structured JSON |
|
|
| All data is algorithmically generated. No real individuals or businesses contributed data. |
|
|
| ### What's Included |
|
|
| - **Account holders:** Business entities (companies, partnerships, corporations) |
| - **Transaction patterns:** B2B payments, employee salaries, vendor payments, business expenses |
| - **Regional diversity:** Major Indian metros |
| - **Temporal patterns:** Quarterly statements, monthly salary cycles, vendor payment patterns |
|
|
| ## Limitations |
|
|
| 1. **No fraud patterns** - Not suitable for fraud detection |
| 2. **Business-only** - No personal/savings account patterns |
| 3. **Urban business focus** - May not represent rural small businesses |
| 4. **Simplified patterns** - Real-world complexity is higher |
| 5. **Format coverage** - Common layouts only, not exhaustive |
| 6. **Synthetic OCR** - May not include all real-world OCR challenges |
|
|
| This dataset is for structure and format learning, not behavioral modeling. Always validate on real data before production deployment. |
|
|
| ## Citation |
|
|
| **BibTeX:** |
|
|
| ```bibtex |
| @dataset{indian_bank_statement_synthetic_2025, |
| author = {AgamiAI Inc.}, |
| title = {Indian Bank Statement Synthetic Dataset}, |
| year = {2025}, |
| publisher = {HuggingFace}, |
| url = {https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements} |
| } |
| ``` |
|
|
| **APA:** |
|
|
| AgamiAI Inc. (2025). *Indian Bank Statement Synthetic Dataset* [Data set]. HuggingFace. https://huggingface.co/datasets/AgamiAI/Indian-Bank-Statements |
|
|
| ## Glossary |
|
|
| **Indian Banking Terms:** |
| - **UPI**: Unified Payments Interface - instant real-time payment system |
| - **NEFT**: National Electronic Funds Transfer - batch processing (half-hourly) |
| - **RTGS**: Real Time Gross Settlement - high-value transactions (₹2 lakh+) |
| - **IMPS**: Immediate Payment Service - instant transfer, 24/7 |
| - **IFSC Code**: Indian Financial System Code - 11-character bank branch identifier |
| - **MICR Code**: Magnetic Ink Character Recognition - 9-digit code for cheque processing |
| - **Current Account**: Business/commercial account, no transaction limits |
|
|
| ## More Information |
|
|
| ### About AgamiAI |
|
|
| AgamiAI builds private AI solutions for enterprises where privacy, accuracy, and compliance are critical. Specialized in Finance, Healthcare, Legal, and Consulting. |
|
|
| Visit: **https://www.agami.ai** |
|
|
| ### File Structure |
|
|
| Each statement includes: |
| - `[statement_id].pdf` - Scanned bank statement |
| - `[statement_id].json` - Structured data with full metadata |
|
|
| ### Related Datasets |
|
|
| Part of AgamiAI's Indian Financial Documents collection: |
| - **Indian Bank Statements** (this dataset) |
| - Indian GST Documents (coming soon) |
| - Indian Tax Documents (coming soon) |
| - Indian Audited Financial Documents (coming soon) |
|
|
| ### Contact |
|
|
| - **Website**: https://www.agami.ai |
| - **HuggingFace**: https://huggingface.co/AgamiAI |
|
|
| --- |
|
|
| **Version:** 1.0.0 | **License:** Apache 2.0 | **Last Updated:** November 2025 |
|
|
| **Privacy Notice:** Entirely synthetic data. No real personal or financial information included. |
